LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data

Sun Jiehuan, Herazo-Maya Jose D., Wang Jane-Ling, Kaminski Naftali and Zhao Hongyu ()
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Sun Jiehuan: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
Herazo-Maya Jose D.: Internal Medicine: Pulmonary, Critical Care and Sleep Medicine, Yale School of Medcine, New Haven, CT, USA
Wang Jane-Ling: Department of Statistics, University of California, Davis, CA, USA
Kaminski Naftali: Internal Medicine: Pulmonary, Critical Care and Sleep Medicine, Yale School of Medcine, New Haven, CT, USA
Zhao Hongyu: Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 2, 9

Abstract: Longitudinal genomics data and survival outcome are common in biomedical studies, where the genomics data are often of high dimension. It is of great interest to select informative longitudinal biomarkers (e.g. genes) related to the survival outcome. In this paper, we develop a computationally efficient tool, LCox, for selecting informative biomarkers related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome. We show that LCox has improved performance compared to existing methods through extensive simulation studies. In addition, by applying LCox to a dataset of patients with idiopathic pulmonary fibrosis, we are able to identify biologically meaningful genes while all other methods fail to make any discovery. An R package to perform LCox is freely available at https://CRAN.R-project.org/package=LCox.

Keywords: biomarker identification; longitudinal gene expression data; survival outcomes (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/sagmb-2017-0060

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